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Faint coronal structures and the possibilities of visualization . Marcel Bělík (1) Miloslav Druckmuller (2) Eva Marková (1) Ladislav Křivský (1) (1) Observatory Úpice, Czech Rep. (2) VUT Brno, Czech Rep. What is the main goal?.
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Faint coronal structures and the possibilities of visualization Marcel Bělík (1) Miloslav Druckmuller (2) Eva Marková (1) Ladislav Křivský (1) (1) Observatory Úpice, Czech Rep. (2) VUT Brno, Czech Rep.
What is the main goal? To get all possible information from the unique picture or the set of pictures !! What can we do ?? We can: • try to use the radial filter • try our potentiality to redraw the details from the individual negatives • try our potentiality in dark room • try to compose several pictures by using „digital dark room“ • try to use the adaptive numerical methods for solar eclipse pictures processing
On the individual picture of the total solar eclipse we can see: But … we can see more and more:
Using of the radial filter Newkirk, G., Jr.: 1967, Ann.Rev.Astron.Astrophys., 5, 231. • grey filter with grade density (~ decrease of coronal brightness) • located in focal plane in front of photographic plate The difficulties: • long exposure times • necessity of very accurate orientation of all • axes in optical system • difference between theoretical and • practical decrease of coronal brightness Redraw the structures from the individual negatives In the case of very precise redraw - not so bad method It is relative accurate, but … very elaborated
„Dark room“ processing • composition of big number of short exposure pictures • „dark room magic experiments“ Both used methods are very laborious and practicallynonreprocessed Composition of several pictures in „digital dark room“
Composition of several pictures in „digital dark room“ This method use some picture operations like radial blur, unsharp masking, rotations, masking and so on to get as realistic as possible picture of all visible corona. The main problem of all these method is the creation of picture artefacts, which could be exchanged with real events. All these methods prefere radial or tangential directions on the picture moreover.
Numerical methods for solar eclipse pictures processing There exist several, let us say, classical solutions of the mentioned problem. Themost powerful are nonlinear pixel value transformations based on image histogram analysis.The best known one is the so called histogram equalization. Another solution usesthe two-dimensional discrete Fourier transform. While any phase spectra manipulationcauses a significant image degradation, amplitude spectra modification is for humaneye generally acceptable and it may increase the subjective quality of an image. It canbe successfully used in visualization of images with high dynamic range. Even if thesemethods are useful in many branches of scientific imaging methods their abilities arevery limited.
Adaptive numerical methods for solar eclipse pictures processing The human vision itself gives a lot of motivation for numerical image processing.The most important feature of human vision is adaptivity. An image is by human eyenot observed as the whole. It is analyzed in small elements and the parameters assensitivity, focussing, aperture etc. are changed in order to reach optimum local view.The human eye is from technical point of view a diferential analyser and it has onlylimited ability to measure absolute brightness. The comparation of brightness is doneon a picture element neighbourhood which is of variable shape depending on the imagecontent. Mathematical numerical methods which modify the algorithm according tolocal image properties as human eye does are called adaptive filters.
Adaptive numerical methods for solar eclipse pictures processing Adaptive filter for high dynamic range image processing must present several typesof adaptivity: The first type of adaptivity is that of pixel value transformation function.The transformation function is usually derived from image histogram, so the adaptivitymay be achieved by using the local histogram computed on some pixel neighbourhoodinstead of whole image histogram. So called adaptive histogram equalization is oneof these methods based on this principle. The creation of suitable pixel neighbourhoodfor histogram computing is very important. Any of fixed type neighbourhood (for examplesquare) cannot give good results because it does not respect boundaries betweenareas with significantly dierent histograms. Therefore it is necessary to construct aneighbourhood according to local properties of image i.e. to use adaptive neighbourhood.This is the second very important type of adaptivity. The third type of adaptivityis based on additive noise analysis. The pixel value transformation function must becorrected according to local parameters, usually according to standard deviation ofadditive noise. If the noise is independent on the image the standard deviation can beestimated by means ofautocorrelation function analysis.
Adaptive numerical methods for solar eclipse pictures processing Angola 2001, Observatory Úpice
Adaptive numerical methods for solar eclipse pictures processing Venezuela 1998, Observatory Úpice Hungary 1999, Miloslav Druckmuller
Conclusion There is a lot of different methods to extract detail structures of eclipse white-light solar corona. Some of them product very interested results, but this is made to the prejudice of any other picture parameters (resolution, photometry, etc.) It seems adaptive numerical methods for solar eclipse pictures processing are the best method for detailed analyze of coronal structure (and probably not only for it). All images published in thepart „Adaptive numerical methods for solar eclipse pictures processing“ were processed using the scientific image analyzer ACC (Adaptive Contrast Control) version 4.0 which is the product of Czech firm SOFO *. This PC software consists of a lot of procedures for exact image processing including the unique ACC algorithm for nonlinear local contrast transformation. *Miloslav Druckmuller is a member of the team developing the ACC image analyzer. Image analyzer ACC (Adaptive Contrast Control) This paper was supported by the grant No. 205/01/0420 GAČR.